{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis to calculate the Green-Kubo conductivity of molten NaCl\n",
    "\n",
    "Assumes the path to PyLAT/src has been added to PYTHONPATH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Classes\n",
    "\n",
    "import and initialize required classes for calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from getAtomCharges import getatomcharges\n",
    "from calcCond import calcCond    \n",
    "\n",
    "\n",
    "gc = getatomcharges()\n",
    "cc = calcCond()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define input \n",
    "input for Green-Kubo conductivity calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = {}\n",
    "datfilename = 'mol.data'\n",
    "trjfilename=['mol.lammpstrj']\n",
    "logfilename='log.lammps'\n",
    "temp=1300\n",
    "nummoltype=[864,864] #number of each molecule type\n",
    "moltypel=['Na','Cl'] #list of all molecule types\n",
    "moltype = []\n",
    "for i in range(0,len(moltypel)):\n",
    "    for j in range(0,nummoltype[i]):\n",
    "        moltype.append(int(i))\n",
    "verb=2\n",
    "GKC_skip=0\n",
    "GKC_Tolerance=0.001\n",
    "GKC_J_Output=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminary Calculations\n",
    "Preliminary calculations for GKC calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "output['Conductivity'] = {}\n",
    "output['Conductivity']['units'] = 'S/m'\n",
    "n = gc.findnumatoms(datfilename)\n",
    "(molcharges, atomcharges,n) = gc.getmolcharges(datfilename,n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Property Calculations\n",
    "Calculation of Green-Kubo conductivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beginning COM velocity calculation\n",
      "COM velocity calculation 47.10% complete"
     ]
    }
   ],
   "source": [
    "output = cc.calcConductivity(molcharges, trjfilename, logfilename, datfilename, temp, output, moltype, moltypel,verb,GKC_skip,GKC_Tolerance,GKC_J_Output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Cumulative Integral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc606a08410>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc606a083d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "Cumulative_Int = output['Conductivity']['GK_Integral']\n",
    "time = output['Conductivity']['Time']\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.xlabel('t (fs)')\n",
    "plt.ylabel(r'$\\int_{0}^{\\infty}\\langle J(t)\\cdot J(0)\\rangle dt$')\n",
    "plt.plot(time,Cumulative_Int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Conductivity: 395.711812212 S/m\n",
      "Contrinution of Na: 240.048777457 S/m\n",
      "Contrinution of Cl: 155.663034755 S/m\n"
     ]
    }
   ],
   "source": [
    "print('Total Conductivity: {} {}'.format(output['Conductivity']['Green_Kubo'],output['Conductivity']['units']))\n",
    "print('Contrinution of Na: {} {}'.format(output['Conductivity']['Green_Kubo_Na'],output['Conductivity']['units']))\n",
    "print('Contrinution of Cl: {} {}'.format(output['Conductivity']['Green_Kubo_Cl'],output['Conductivity']['units']))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
